Continuous data is a type of data that can take on any value within a certain range or set of ranges. It is opposed to discrete data, which can only take on a specific set of values.
- Temperature in degrees Celsius
- Height in meters
- Weight in kilograms
- Distance in kilometers
Continuous data can be either numerical or ordinal. Numerical continuous data is data that is measured on a scale and can be ordered according to size. Ordinal CD is data that is ranked or ordered, but the distances between the values are not necessarily equal.
It is often used in statistical analyses, such as regression analysis and analysis of variance (ANOVA), to examine the relationship between variables and to test hypotheses about the population from which the data was collected. Continuous data can be plotted on a graph using a scale, such as a line graph or scatter plot.
It is often approximated using discrete data in practical situations. For example, although the temperature can be measured continuously in degrees Celsius, it is often rounded to the nearest whole number when recorded. Similarly, although weight can be measured continuously in kilograms, it is often recorded in increments of 0.5 or 1 kilogram.
Examples of Continuous Data
One example of continuous data is the height of a group of people. Height can be measured in units of length, such as meters or inches, and can take on any value within a certain range. For example, a person’s height could be 1.75 meters, 1.76 meters, 1.755 meters, etc. In this case, the height data is numerical and continuous.
Another example of continuous data is the time it takes for a person to complete a task, such as running a marathon. Time can be measured in units of time, such as seconds, minutes, or hours, and can take on any value within a certain range. For example, the time it takes to complete a marathon could be 3 hours, 2 hours 59 minutes, 2 hours 59 minutes 30 seconds, etc. In this case, the time data is also numerical and continuous.